Python 3.11.0 | packaged by conda-forge | (main, Jan 16 2023, 14:12:30) [MSC v.1916 64 bit (AMD64)]
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IPython 8.10.0 -- An enhanced Interactive Python. Type '?' for help.

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega3')
df = get_data_frame(MEASURE_FOLDER)
# df.drop(['CATbkBool',
# 	'CATnoLightBackground', 'CATbkAvg', 'CATprop', 'reloadStartInd',
# 	'reloadEndInd'], axis=1,inplace=True)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

min_ratios = df_grouped['ratio'].min()
# Iterate through the groups and create separate DataFrames

groups = dict(list(df_grouped))


dfs = [df for df in groups.values()]
max_freqs = [384182.5]*15
#max_freqs=[384178.599, 384179.091, 384184.733]
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq)  in enumerate(zipped_data[:]):
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
    ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}", linewidth=2.5)

plt.legend()

100%|██████████| 205/205 [00:04<00:00, 47.73it/s]

<matplotlib.legend.Legend at 0x1fdb465b650>

plt.plot(min_ratios, '-o')

[<matplotlib.lines.Line2D at 0x1fdb4a25c50>]

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega4')
df = get_data_frame(MEASURE_FOLDER)
# df.drop(['CATbkBool',
# 	'CATnoLightBackground', 'CATbkAvg', 'CATprop', 'reloadStartInd',
# 	'reloadEndInd'], axis=1,inplace=True)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

min_ratios = df_grouped['ratio'].min()
# Iterate through the groups and create separate DataFrames

groups = dict(list(df_grouped))


dfs = [df for df in groups.values()]
max_freqs = [384182.5]*15
#max_freqs=[384178.599, 384179.091, 384184.733]
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq)  in enumerate(zipped_data[:]):
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
    ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}", linewidth=2.5)

plt.legend()

100%|██████████| 205/205 [07:56<00:00,  2.33s/it]

<matplotlib.legend.Legend at 0x1fdb48bffd0>

plt.plot(min_ratios, '-o')

[<matplotlib.lines.Line2D at 0x1fdb496c410>]

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega5')
df = get_data_frame(MEASURE_FOLDER)
# df.drop(['CATbkBool',
# 	'CATnoLightBackground', 'CATbkAvg', 'CATprop', 'reloadStartInd',
# 	'reloadEndInd'], axis=1,inplace=True)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

min_ratios = df_grouped['ratio'].min()
# Iterate through the groups and create separate DataFrames

groups = dict(list(df_grouped))


dfs = [df for df in groups.values()]
max_freqs = [384182.5]*15
#max_freqs=[384178.599, 384179.091, 384184.733]
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq)  in enumerate(zipped_data[:]):
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
    ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}", linewidth=2.5)

plt.legend()

100%|██████████| 205/205 [08:25<00:00,  2.47s/it]

<matplotlib.legend.Legend at 0x1fdb483d250>

plt.plot(min_ratios, '-o')

[<matplotlib.lines.Line2D at 0x1fdb48b5c50>]

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega3')
df = get_data_frame(MEASURE_FOLDER)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

groups = dict(list(df_grouped))

dfs1 = [df for df in groups.values()]
min_ratios1 = df_grouped['ratio'].min()

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega4')
df = get_data_frame(MEASURE_FOLDER)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

groups = dict(list(df_grouped))

dfs2 = [df for df in groups.values()]
min_ratios2 = df_grouped['ratio'].min()

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega5')
df = get_data_frame(MEASURE_FOLDER)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

groups = dict(list(df_grouped))

dfs3 = [df for df in groups.values()]
min_ratios3 = df_grouped['ratio'].min()

100%|██████████| 205/205 [00:04<00:00, 48.81it/s]
100%|██████████| 205/205 [00:02<00:00, 73.33it/s] 
100%|██████████| 205/205 [00:02<00:00, 76.38it/s]

plt.plot(min_ratios2, '-o', label=r'$\delta = -12$ MHz')
plt.plot(min_ratios1, '-o', label=r'$\delta = -10$ MHz')
plt.plot(min_ratios3, '-o', label=r'$\delta = -8$ MHz')

plt.legend()

<matplotlib.legend.Legend at 0x1fdb146b090>

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega2')
df = get_data_frame(MEASURE_FOLDER)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

groups = dict(list(df_grouped))

dfs4 = [df for df in groups.values()]
min_ratios4 = df_grouped['ratio'].min()

100%|██████████| 411/411 [00:03<00:00, 107.49it/s]

plt.plot(min_ratios2, '-o', label=r'$\delta = -12$ MHz')
plt.plot(min_ratios1, '-o', label=r'$\delta = -10$ MHz')
plt.plot(min_ratios4, '-o', label=r'$\delta = -10$ MHz')
plt.plot(min_ratios3, '-o', label=r'$\delta = -8$ MHz')

plt.legend()

<matplotlib.legend.Legend at 0x1fdb7e5b6d0>

plt.plot(min_ratios2, '-o', label=r'$\delta = -12$ MHz (T2)')
plt.plot(min_ratios1, '-o', label=r'$\delta = -10$ MHz (T1)')
plt.plot(min_ratios3, '-o', label=r'$\delta = -8$ MHz (T3)' )

plt.legend()

<matplotlib.legend.Legend at 0x1fdb55af310>

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega')
df = get_data_frame(MEASURE_FOLDER)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

groups = dict(list(df_grouped))

dfs5 = [df for df in groups.values()]
min_ratios5 = df_grouped['ratio'].min()

100%|██████████| 617/617 [00:03<00:00, 180.63it/s]

plt.plot(min_ratios2, '-o', label=r'$\delta = -12$ MHz', color='red')
plt.plot(min_ratios5, '-o', label=r'$\delta = -10$ MHz', color='orange')
plt.plot(min_ratios1, '-o', label=r'$\delta = -10$ MHz', color='blue')
plt.plot(min_ratios4, '-o', label=r'$\delta = -10$ MHz', color='darkblue')
plt.plot(min_ratios3, '-o', label=r'$\delta = -8$ MHz', color='green')

plt.legend()

<matplotlib.legend.Legend at 0x1fdb73d5590>

for df in dfs1:
    plt.plot(df['motR'], '-')

for df in dfs2:
    plt.plot(df['motR'], '-')

for df in dfs3:
    plt.plot(df['motR'], '-')

for df in dfs4:
    plt.plot(df['motR'], '-')

for df in dfs5:
    plt.plot(df['motR'], '-')

MEASURE_FOLDER = os.path.join(EXP_FOLDER, 'testPArunMega2')
df = get_data_frame(MEASURE_FOLDER)
# df.drop(['CATbkBool',
# 	'CATnoLightBackground', 'CATbkAvg', 'CATprop', 'reloadStartInd',
# 	'reloadEndInd'], axis=1,inplace=True)
df.dropna(inplace=True)

df_grouped = df.groupby(by='pump_reference')
grouped_dataframes = {}

min_ratios = df_grouped['ratio'].min()
# Iterate through the groups and create separate DataFrames

groups = dict(list(df_grouped))


dfs = [df for df in groups.values()]
max_freqs = [384182.5]*15
#max_freqs=[384178.599, 384179.091, 384184.733]
zipped_data = list(zip(dfs, max_freqs))
fig, ax = plt.subplots()
for i, (df, max_freq)  in enumerate(zipped_data[:]):
    data = df.dropna()
    freqs = ((max_freq-PUMP_FREQUENCY)-(data['tempV']-data['tempV'].min())*FREQVSVOLT- (data['currV']-data['currV'].min())*FREQVSCURR)
    ax=plot_spline_fit(ax, x=freqs, y=data['ratio'], yerr=data['ratioErr'],scolor=f'C{i}', mfc=f'C{i}',color=f'C{i}', s=0.0, ms=5, figsize=(10, 10), label=f"Pump Amplituide = { df.iloc[10]['pump_reference'] :.2f}", linewidth=2.5)

plt.legend()

100%|██████████| 411/411 [00:03<00:00, 114.73it/s]

<matplotlib.legend.Legend at 0x1fdc19336d0>

plt.plot(min_ratios, '-o')

[<matplotlib.lines.Line2D at 0x1fdc0bf72d0>]

for df in dfs5:
    plt.plot(np.arange(41), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc0d71d10>

for df in dfs1:
    plt.plot(np.arange(41), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc123b6d0>

for df in dfs2:
    plt.plot(np.arange(41), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc11f9d10>

for df in dfs3:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc6105590>

for df in dfs3:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc64bb6d0>

for df in dfs1:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()
plt.show()
plt.close()
for df in dfs1:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc73d5590>

for df in dfs2:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()
plt.show()
plt.close()
for df in dfs1:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc76836d0>

for df in dfs2:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()
plt.show()
plt.close()
for df in dfs2:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc7720c50>

for df in dfs1:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()
plt.show()
plt.close()
for df in dfs1:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc8d05590>

for df in dfs3:
    plt.plot(np.arange(len(df['motR'])), df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()
plt.show()
plt.close()
for df in dfs3:
    plt.plot( df['motR'], '-', label=f"{df.iloc[10]['pump_reference']:.2f}")
plt.legend()

<matplotlib.legend.Legend at 0x1fdc8295590>